Ontology based text mining of gene-phenotype associations: application to candidate gene prediction
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Gene-phenotype associations play an important role in understanding<br> the disease mechanisms which is a requirement for treatment<br> development. A portion of gene-phenotype associations are observed<br> mainly experimentally and made publicly available through several<br> standard resources such as MGI. However, there is still a vast<br> amount of gene--phenotype associations buried in the biomedical<br> literature. Given the large amount of literature data, we need<br> automated text mining tools to alleviate the burden in manual<br> curation of gene-phenotype associations and to develop<br> comprehensive resources. We developed an ontology based<br> approach in combination with statistical methods to text mine<br> gene-phenotype associations from literature. Our method achieved<br> AUC values of 0.90 and 0.75 in recovering known gene-phenotype<br> associations from HPO and MGI respectively. We posit that candidate<br> genes and their relevant diseases should be expressed with similar<br> phenotypes in publications. Thus, we demonstrate the utility of our<br> approach by predicting disease candidate genes based on the semantic<br> similarities of phenotypes associated with genes and diseases. We evaluated our disease candidate prediction model on<br> the gene-disease associations from MGI. Our model achieved AUC<br> values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of<br> gene-disease associations respectively. Our manual analysis on the<br> text mined data revealed that, our method can accurately extract<br> gene-phenotype associations which are not currently covered by the<br> existing public gene-phenotype resources. Overall, results indicate<br> that our method can precisely extract known as well as new<br> gene-phenotype associations from literature. This released dataset at Zenodo covers our gene-phenotype extracts from the literature. All the methods used to extract the data are available at https://github.com/bio-ontology-research-group/genepheno.
